Literature DB >> 21440086

Summarization of clinical information: a conceptual model.

Joshua C Feblowitz1, Adam Wright, Hardeep Singh, Lipika Samal, Dean F Sittig.   

Abstract

BACKGROUND: To provide high-quality and safe care, clinicians must be able to optimally collect, distill, and interpret patient information. Despite advances in text summarization, only limited research exists on clinical summarization, the complex and heterogeneous process of gathering, organizing and presenting patient data in various forms.
OBJECTIVE: To develop a conceptual model for describing and understanding clinical summarization in both computer-independent and computer-supported clinical tasks.
DESIGN: Based on extensive literature review and clinical input, we developed a conceptual model of clinical summarization to lay the foundation for future research on clinician workflow and automated summarization using electronic health records (EHRs).
RESULTS: Our model identifies five distinct stages of clinical summarization: (1) Aggregation, (2) Organization, (3) Reduction and/or Transformation, (4) Interpretation and (5) Synthesis (AORTIS). The AORTIS model describes the creation of complex, task-specific clinical summaries and provides a framework for clinical workflow analysis and directed research on test results review, clinical documentation and medical decision-making. We describe a hypothetical case study to illustrate the application of this model in the primary care setting.
CONCLUSION: Both practicing physicians and clinical informaticians need a structured method of developing, studying and evaluating clinical summaries in support of a wide range of clinical tasks. Our proposed model of clinical summarization provides a potential pathway to advance knowledge in this area and highlights directions for further research.
Copyright © 2011 Elsevier Inc. All rights reserved.

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Year:  2011        PMID: 21440086     DOI: 10.1016/j.jbi.2011.03.008

Source DB:  PubMed          Journal:  J Biomed Inform        ISSN: 1532-0464            Impact factor:   6.317


  36 in total

1.  Development and evaluation of a crowdsourcing methodology for knowledge base construction: identifying relationships between clinical problems and medications.

Authors:  Allison B McCoy; Adam Wright; Archana Laxmisan; Madelene J Ottosen; Jacob A McCoy; David Butten; Dean F Sittig
Journal:  J Am Med Inform Assoc       Date:  2012-05-12       Impact factor: 4.497

2.  Association of Medical Directors of Information Systems consensus on inpatient electronic health record documentation.

Authors:  J Shoolin; L Ozeran; C Hamann; W Bria
Journal:  Appl Clin Inform       Date:  2013-06-26       Impact factor: 2.342

3.  Using phrases and document metadata to improve topic modeling of clinical reports.

Authors:  William Speier; Michael K Ong; Corey W Arnold
Journal:  J Biomed Inform       Date:  2016-04-21       Impact factor: 6.317

4.  Validation of a Crowdsourcing Methodology for Developing a Knowledge Base of Related Problem-Medication Pairs.

Authors:  A B McCoy; A Wright; M Krousel-Wood; E J Thomas; J A McCoy; D F Sittig
Journal:  Appl Clin Inform       Date:  2015-05-20       Impact factor: 2.342

5.  Physician stress and burnout: the impact of health information technology.

Authors:  Rebekah L Gardner; Emily Cooper; Jacqueline Haskell; Daniel A Harris; Sara Poplau; Philip J Kroth; Mark Linzer
Journal:  J Am Med Inform Assoc       Date:  2019-02-01       Impact factor: 4.497

6.  New Unintended Adverse Consequences of Electronic Health Records.

Authors:  D F Sittig; A Wright; J Ash; H Singh
Journal:  Yearb Med Inform       Date:  2016-11-10

7.  Impact of a prototype visualization tool for new information in EHR clinical documents.

Authors:  O Farri; A Rahman; K A Monsen; R Zhang; S V Pakhomov; D S Pieczkiewicz; S M Speedie; G B Melton
Journal:  Appl Clin Inform       Date:  2012-10-31       Impact factor: 2.342

8.  Rapid implementation of inpatient electronic physician documentation at an academic hospital.

Authors:  J S Hahn; J A Bernstein; R B McKenzie; B J King; C A Longhurst
Journal:  Appl Clin Inform       Date:  2012-05-02       Impact factor: 2.342

9.  Development of a clinician reputation metric to identify appropriate problem-medication pairs in a crowdsourced knowledge base.

Authors:  Allison B McCoy; Adam Wright; Deevakar Rogith; Safa Fathiamini; Allison J Ottenbacher; Dean F Sittig
Journal:  J Biomed Inform       Date:  2013-12-07       Impact factor: 6.317

10.  Evaluating topic model interpretability from a primary care physician perspective.

Authors:  Corey W Arnold; Andrea Oh; Shawn Chen; William Speier
Journal:  Comput Methods Programs Biomed       Date:  2015-10-30       Impact factor: 5.428

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